YOLO-SPCI提升遥感目标检测，通过三模块融合增强多尺度特征
YOLO-SPCI: Enhancing Remote Sensing Object Detection via Selective-Perspective-Class Integration
- 引入SPCI模块，自适应调节全局特征流并融合多视角上下文
- 在NWPU VHR-10数据集上优于现有方法，显著提升高分辨率遥感图像检测精度
- 轻量设计适合部署于资源受限的遥感实时分析系统
遥感图像中的目标检测因尺度变化极端、目标密集分布及背景杂乱而极具挑战。尽管近期检测器如YOLOv8表现良好，但其主干网络缺乏显式机制引导多尺度特征优化，限制了在高分辨率航空数据上的性能。本文提出YOLO-SPCI，一种注意力增强的检测框架，引入轻量级选择性视角类别融合（SPCI）模块以改善特征表示。该模块包含三个组件：用于自适应调节全局特征流的选择性通道门（SSG）、用于上下文感知多尺度融合的视角融合模块（PFM），以及增强类间可分性的类别判别模块（CDM）。将两个SPCI模块嵌入YOLOv8主干的P3和P5阶段，实现有效特征精炼的同时保持与原颈部和头部的兼容性。在NWPU VHR-10数据集上的实验表明，YOLO-SPCI相比当前最优检测器取得更优性能。
原文摘要 · Abstract (English)
Object detection in remote sensing imagery remains a challenging task due to extreme scale variation, dense object distributions, and cluttered backgrounds. While recent detectors such as YOLOv8 have shown promising results, their backbone architectures lack explicit mechanisms to guide multi-scale feature refinement, limiting performance on high-resolution aerial data. In this work, we propose YOLO-SPCI, an attention-enhanced detection framework that introduces a lightweight Selective-Perspective-Class Integration (SPCI) module to improve feature representation. The SPCI module integrates three components: a Selective Stream Gate (SSG) for adaptive regulation of global feature flow, a Perspective Fusion Module (PFM) for context-aware multi-scale integration, and a Class Discrimination Module (CDM) to enhance inter-class separability. We embed two SPCI blocks into the P3 and P5 stages of the YOLOv8 backbone, enabling effective refinement while preserving compatibility with the original neck and head. Experiments on the NWPU VHR-10 dataset demonstrate that YOLO-SPCI achieves superior performance compared to state-of-the-art detectors.
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